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At least 217 records · Page 12Linked to original sources
PubMed1995

"Auctoritas" psychiatric expert system shell.

We present a short description of a complex psychiatric computer expert system, including functions that help the physicians and the hospital staff in the administrative, diagnostic, therapeutic, statistical, and scientific work. There are separate data-storing, health insurance-supporting, or simple advisory programs, but we can not avail a system--in our country--that provides us with all these functions together. Hence the aim of our program is to produce a universal computer system that makes the patients' long distance follow-up possible. Our diagnostic expert system shell, which is appropriate for using the symptoms and criteria scheme of the internationally accepted diagnostic systems such as DSM and ICD, helps to archive homogeneous, up-to-date psychiatric nosology; this is essential for the correct diagnostic, statistical, and scientific work. Let us introduce our expert system. It consists of four parts: administration, diagnostic decision support system, activities concerning treatment, and statistics. The part called "Administration" contains all data about actual and emitted in-patients and out-patients, including their particulars and data necessary for health insurance (duration of treatment, diagnosis); here we find and edit medical documents. The most important part of the "Auctoritas" system is the "Diagnostic decision support system." In practice, expert systems use decision trees with yes-no logic, fuzzy logic, and pattern matching on the basis of the method of deduction; and backward chaining or forward chaining on the basis of the direction of deduction. Our system uses the methods of fuzzy logic and backward chaining. In other medical disciplines, good results are achieved by applying the pattern matching method; to make validity and verification researches, however, these systems are inappropriate. The diagnoses relying on the up-to-date psychiatric diagnostic systems--DSM-IV and ICD-X--are based on classical logic and can be correctly validated and verified. Hence we have chosen the fuzzy logic, which is the up-to-date extension of classical logic and influences the validity and verification researches, for the construction of our system. The diagnostic part is a shell that can be filled up optionally with knowledge bases of the DSM-IV, ICD-X, or other diagnostic systems and has the following structure. The diagnostic course is biphased as we can differ symptoms and criteria (duration of the illness, aethyological factors). We managed to extend the traditional applications using yes-no logic with three factors that make the system more sensitive and flexible. These are the scaling, sorting by importance of symptoms, and reliability-validity results. The "scaling" means that the physician scales the input symptoms by severity; this influences the statistical probability of the possible diagnoses. "Sorting by importance" is gauging certain symptoms by importance in a syndrome. Finally the third point, "reliability-validity results," means taking account of the latest validity values of a certain disorder of the used diagnostic system--according to the latest validity researches--and the diagnostical reliability of our expert system. The "Activity concerning treatment" is a practical part of our program that contents the examination and therapy scheduling and monitoring results. Under the point of "Statistics," we can prepare all data of the patients in various ways. In summary, the "Auctoritas" computer system is a global database managing the newly-developed advisory system; it is appropriate for managing a complete hospital network system for the continuing individual long-distance observation of patients. It collects all the necessary information of one patient in one file. The long-term benefit is that it can compile and process large amounts of information about the patients and help physicians come to scientific conclusions for research and publications.

Decision Making, Computer-Assisted↗
PubMed1997

The knowledge model of MedFrame/CADIAG-IV.

The medical consultation system MedFrame/CADIAG-IV is a successor of the prior CADIAG projects. It is the result of a complete redesign to account for today's demands on state-of-the-art software. Its knowledge representation and inference process are based on fuzzy set theory and fuzzy logic. Fuzzy sets are used for conversions from measured numeric values and observational data into symbolic ones. Medical relationships between findings, diseases, and therapies, the rules, are represented by fuzzy relations, that express positive or negative associations. Findings, diseases, and therapies are organised in hierarchies.

Artificial Intelligence↗
PubMed2000

Trend recognition in clinical signals using template-based methods.

The recognition of clinically significant trends in monitored signals plays an important role in many medical diagnostic applications. A template-based system technique to identify characteristic patterns in time-series data is described, based on fuzzy logic. Fuzzy set theory allows the creation of fuzzy templates from linguistic rules. The resulting fuzzy template system can accommodate multiple time signals, relative or absolute trends, and automatically generates a normalised "goodness of fit" score. The template approach was originally developed for monitoring during anaesthesia but has the potential to be useful in other domains that require temporal pattern recognition.

Artificial Intelligence↗
PubMed2002

Fuzzy pharmacology: theory and applications.

Fuzzy pharmacology is a term coined to represent the application of fuzzy logic and fuzzy set theory to pharmacological problems. Fuzzy logic is the science of reasoning, thinking and inference that recognizes and uses the real world phenomenon that everything is a matter of degree. It is an extension of binary logic that is able to deal with complex systems because it does not require crisp definitions and distinctions for the system components. In pharmacology, fuzzy modeling has been used for the mechanical control of drug delivery in surgical settings, and work has begun evaluating its use in other pharmacokinetic and pharmacodynamic applications. Fuzzy pharmacology is an emerging field that, based on these initial explorations, warrants further investigation.

Animals↗
PubMed2002

Fuzzy logic-based tumor-marker profiles improved sensitivity in the diagnosis of lung cancer.

BACKGROUND: The aim of this study was to improve the diagnostic efficiency of tumor markers in the diagnosis of lung cancer, by the mathematical evaluation of a tumor marker profile employing fuzzy logic modelling. METHODS: A panel of four tumor markers, i.e., carcinoembryonic antigen (CEA), cytokeratin 19 antibody (CYFRA 21-1), neuron-specific enolase (NSE), squamous cell carcinoma-related antigen (SCC) and, additionally, C-reactive protein (CRP), was measured in 175 newly diagnosed lung cancer patients with different histological types and stages. Results were compared with those in 120 control subjects, including 27 with chronic obstructive pulmonary diseases (COPD), 65 with pneumoconiosis, and 11 persons with acute inflammatory lung diseases. A classificator was developed using a fuzzy-logic rule-based system. RESULTS: Application of the fuzzy-logic rule-based system to the tumor marker values of CYFRA 21-1, NSE, and CRP yielded an increase in sensitivity of approximately 20%, i.e., 92%, compared with that of the best single marker, CYFRA 21-1(sensitivity, 72%). The corresponding specificity was 95%. The fuzzy classificator significantly improved the sensitivity of the tumor marker panel in stages I and IIIa for non-small-cell lung cancer, as well as in "limited disease" status for small-cell lung cancer. Also, the diagnosis of other stages of lung cancer was enhanced. CONCLUSION: Fuzzy-logic analysis was proven to be more powerful than the measurement of single markers alone or combinations using multiple logistic regression analysis of all markers. Therefore, fuzzy logic offers a promising diagnostic tool to improve tumor marker efficiency.

Aged↗
PubMedDate not supplied

Fuzzy logic-based tumor marker profiles including a new marker tumor M2-PK improved sensitivity to the detection of progression in lung cancer patients.

In lung cancer patients tumor markers are used for disease monitoring. The goal of this study was to improve diagnostic efficiency in the detection of tumor progression in lung cancer patients by using fuzzy logic modeling in combination with a tumor marker panel (Tumor M2-PK, CYFRA 21-1, CEA, NSE and SCC). Thirty-three small cell lung cancers (SCLC) and 69 consecutive inoperable patients (40 squamous and 29 adenocarcinomas) were included in a prospective study. The changes of blood levels of tumor markers as well as their analysis by fuzzy logic modelling were compared to the clinical evaluation of response vs. non-response to therapy. Clinical monitoring was evaluated according to the standard criteria of the WHO. Tumor M2-PK was measured in plasma with an ELISA (ScheBo Biotech, Germany) and all other markers in sera (Roche, Germany). At a 90% specificity, the respective best single marker found the following fraction of all patients who had tumor progression clinically detected: in SCLC with NSE 52%, in adenocarcinoma with CYFRA 21-1 89% and in squamous carcinoma with SCC 65%. A fuzzy logic rule-based system employing a tumor marker panel increased the sensitivity in small cell carcinomas to 73% with the marker combination NSE/CEA and to 63% with the marker combination NSE/Tumor M2-PK, respectively. In squamous carcinomas an improvement of sensitivity is also observed using the marker combination of SCC/Tumor M2-PK (Sensitivity: 81%) or SCC/CEA (Sensitivity: 71%). By using the fuzzy logic method and the marker combination CYFRA 21-1/CEA as well as CYFRA 21-1/Tumor M2-PK, the detection of lung cancer progression was possible in all adenocarcinomas. With the fuzzy logic method and a tumor marker panel (including the new marker Tumor M2-PK), a useful diagnostic tool for the detection of progression in lung cancer patients is available.

Adenocarcinoma↗
PubMedDate not supplied

Improved sensitivity in the diagnosis of gastro-intestinal tumors by fuzzy logic-based tumor marker profiles including the tumor M2-PK.

The aim of this study was to improve diagnostic efficiency in the detection of gastro-intestinal cancers by using fuzzy logic modeling in combination with a tumor marker panel (CEA, CA72-4, CA19-9) including Tumor M2-PK. In this prospective study histologically confirmed colorectal (n=247), esophageal (n=86) and gastric cancer (n=122) patients were investigated and compared to control (n=53) persons without any malignant diseases. Tumor M2-PK was measured in plasma with an ELISA (ScheBoBiotech, Germany); all other markers were measured in sera (Roche, Germany). At 95% specificity, tumor detection was possible by the best single marker in colorectal cancer patients in 48% (Tumor M2-PK), in gastric cancers in 61% (CA72-4) and in esophageal cancers in 56% (Tumor M2-PK). A fuzzy logic rule-based system employing a tumor marker panel increased sensitivity significantly in colorectal cancers (p<0. 001) to 63% (Tumor M2-PK and CEA), in gastric cancers (p<0.001) to 81% (Tumor M2-PK and CA 72-4) and in esophageal cancers (p<0.02) to 74% (Tumor M2-PK and CA72-4). Adding a third marker further improved the sensitivity only marginally. Fuzzy logic analysis has proven to be more powerful than measurement of single markers alone or combinations using multiple logistic regression analysis of the markers. Therefore, with the fuzzy logic method and a tumor marker panel (including Tumor M2-PK), a new diagnostic tool for the detection of gastro-intestinal cancers is available.

Aged↗
PubMed2003

Fuzzy logic-based tumor marker profiles improved sensitivity of the detection of progression in small-cell lung cancer patients.

Tumor markers were used for disease monitoring in small-cell lung cancer patients. The aim of this study was to improve diagnostic efficiency in the detection of tumor progression in small-cell lung cancer patients by using fuzzy logic modeling in combination with a tumor marker panel (NSE, ProGRP, Tumor M2-PK, CYFRA 21-1, and CEA). Thirty-three consecutive small-cell lung cancer patients were included in a prospective study. The changes in blood levels of tumor markers and their analysis by fuzzy logic modeling were compared with the clinical evaluation of response versus non-response to therapy. Clinical monitoring was performed according to the standard criteria of the WHO. Tumor M2-PK was measured in plasma with an ELISA, all other markers were measured in sera. At 90% specificity, clinically detected tumor progression was found by the best single marker, NSE, in 32% of all cases. A fuzzy logic rule-based system employing a tumor marker panel increased the sensitivity significantly (P>0.0001) in small-cell carcinomas to 67% with the threemarker combination NSE/ProGRP/Tumor M2-PK and to 56% with the best two-marker combination ProGRP/Tumor M2-PK, respectively. An improvement of sensitivity was also observed using the two-marker combination of ProGRP/NSE (sensitivity 49%) or NSE/Tumor M2-PK (sensitivity 52%). The fuzzy classifier was able to detect a higher rate of progression in small-cell lung cancer patients compared with the multiple logistic regression analysis using the marker combination NSE/ProGRP/Tumor M2-PK (sensitivity 44%; AUC=0.76). With the fuzzy logic method and different tumor marker panels (NSE, ProGRP and Tumor M2-PK), a new diagnostic tool for the detection of progression in patients with small-cell lung cancer is available.

Aged↗
PubMed1998

Tumour markers in the diagnosis of bronchial carcinoma: new options using fuzzy logic-based tumour marker profiles.

The diagnosis of lung cancer and early knowledge of its histological type are very important; however, this is still a difficult subject for the physician. The aim of this study was to improve the diagnostic efficiency of tumour markers in the diagnosis of bronchial carcinoma by mathematical evaluation of a tumour marker profile employing fuzzy logic modeling. A panel of five tumour markers, including CYFRA 21-1, CEA, NSE, and five additional parameters was determined in 281 patients with confirmed primary diagnosis of bronchial carcinoma of different histology and stage. A further 131 persons, who had acute and chronic benign lung diseases, served as a control group. A classificator was developed using a fuzzy-logic rule-based system. The diagnostic value of the combined tumour markers was significantly better than that of the individual markers and of a combination of CYFRA 21-1, CEA, and NSE. The discrimination of malignant vs benign diseases was realized with a sensitivity of 87.5% and specificity of 85.5%. The rate of correct classification of small-cell vs non-small-cell lung carcinoma was 90.6% and 91.1%, respectively; for squamous cell carcinoma vs adenocarcinoma it was 76.8% and 78.8%, respectively. Our detailed analysis has shown that the fuzzy logic system improves diagnostic accuracy up to a rate of 20%, especially in early stages and in patients with all marker levels in the grey area. Our concept proved to be more powerful than measurement of single markers or the combination of CEA, CYFRA 21-1, and NSE. Its use may help in distinguishing between malignant and benign disease and make it possible to define different subgroups of patients earlier in the course of their disease.

Adolescent↗
PubMed1994

MEDUSA: a fuzzy expert system for medical diagnosis of acute abdominal pain.

Even today, the diagnosis of acute abdominal pain represents a serious clinical problem. The medical knowledge in this field is characterized by uncertainty, imprecision and vagueness. This situation lends itself especially to be solved by the application of fuzzy logic. A fuzzy logic-based expert system for diagnostic decision support is presented (MEDUSA). The representation and application of uncertain and imprecise knowledge is realized by fuzzy sets and fuzzy relations. The hybrid concept of the system enables the integration of rule-based, heuristic and case-based reasoning on the basis of imprecise information. The central idea of the integration is to use case-based reasoning for the management of special cases, and rule-based reasoning for the representation of normal cases. The heuristic principle is ideally suited for making uncertain, hypothetical inferences on the basis of fuzzy data and fuzzy relations.

Abdomen, Acute↗
PubMed1995

Adaptive fuzzy control of unstable nonlinear systems.

This paper addresses the structure and an associated on-line learning algorithm of a feedforward multilayer connectionist network for realizing the basic elements and functions of a traditional fuzzy logic controller. The proposed Fuzzy Adaptive Learning Control Network (FALCON) can be contrasted with the traditional fuzzy logic control systems in their network structure and learning ability. An on-line structure/parameter learning algorithm, called FALCON-ART, is proposed for constructing the FALCON dynamically. The FALCON-ART can partition the input/output space in a flexible way based on the distribution of the training data. Hence it can avoid the problem of combinatorial growing of partitioned grids in some complex systems. It combines the backpropagation learning scheme for parameter learning and the fuzzy ART algorithm for structure learning. More notably, the FALCON-ART can on-line partition the input/output spaces, tune membership functions, and find proper fuzzy logic rules dynamically without any a priori knowledge or even any initial information on these. The proposed learning scheme has been successfully used to control two unstable nonlinear systems. They are the seesaw system and the inverted wedge system.

Algorithms↗
PubMed2003

Increased predictive value of parameters by fuzzy logic-based multiparameter analysis.

BACKGROUND: A recent study on postoperative effusions and edema was used to demonstrate the potential of fuzzy techniques in multiparameter data analysis. In this study, more than 50 parameters of 75 patients were collected and examined for correlations between some of the parameters and the later development of complications. METHODS: We employed a rule-based fuzzy-logic system in order to combine the diagnostic values of single parameters. The advantage of fuzzy sets is that they substitute sharp cut-off values with a smooth transition from one property to another. Therefore, there is no decision of "either-or" but rather a graded assessment of "more or less", which is often more suitable for a problem. RESULTS: The fuzzy combination of parameters led to a large increase of sensitivity and specificity when compared with the best single parameter. This increase was achieved by taking a close look at the parameters. A newly created parameter, relative weight, turned out to be very powerful. CONCLUSIONS: Fuzzy techniques can increase the discriminating power of classical statistical tools. In addition, results obtained by fuzzy analysis are highly interpretable. A combination of the CLASSIF1 algorithm for the identification of the most relevant parameters, followed by fuzzy analysis, represents a powerful tool for the handling of large amounts of multiparameter data.

Adolescent↗
PubMed2006

A fuzzy model of glucose regulation.

We present a detailed glucose regulation model using fuzzy inference system (FIS) descriptions of hormonal control action and the familiar Michaelis-Menten (M-M) kinetic description for glucose transport. The fuzzy M-M model is compared and contrasted with a well-known comprehensive glucose model. The two models give similar results for glucose response, endogenous glucose production, and total uptake. The fuzzy M-M model features a renal subsystem that provides 25% of the endogenous glucose production. The work demonstrates the successful application of fuzzy logic and fuzzy inference to biological modelling. The flexibility of fuzzy inference, a linguistic description technique, permits conceptually simple statements about nonlinear processes.

Biological Transport↗